PySpark’s instr Function: Substring searches in Big Data

The instr function in PySpark’s DataFrame API helps in determining the position of the first occurrence of a substring within a string. It returns an integer value, starting from 1, which signifies the first position of the substring. If the substring is not found, instr returns 0. This article delves deep into the instr function, presenting its applications, benefits, and scenarios where it’s indispensable.

Sample code

from pyspark.sql import SparkSession
from pyspark.sql.functions import instr

# Initialize SparkSession
spark = SparkSession.builder \
    .appName("InstrFunctionDemo @ Freshers.in") \
    .getOrCreate()

# Sample data
data = [("The Matrix Reloaded",),
        ("Star Wars: The Last Jedi",),
        ("Jurassic Park",),
        ("The Great Gatsby",)]

# Define DataFrame
df = spark.createDataFrame(data, ["title"])

# Use the instr function to find the position of the word "The" in each title
df_with_position = df.withColumn("position_of_The", instr(df["title"], "The"))
df_with_position.show()

Output

+--------------------+---------------+
|               title|position_of_The|
+--------------------+---------------+
| The Matrix Reloaded|              1|
|Star Wars: The La...|             12|
|       Jurassic Park|              0|
|    The Great Gatsby|              1|
+--------------------+---------------+

Using the instr Function

1. Text Analytics: When parsing through large volumes of text, instr can be employed to find the position of keywords or phrases.
2. Data Cleaning: To identify rows where specific keywords exist, which might be erroneous or outliers.
3. Feature Engineering: Extract specific patterns from text data, e.g., detecting if a string has “http://” to categorize it as a URL.
4. Log Analysis: When working with log files, finding specific error codes or tags within log messages.

Benefits of using the instr function:

1. Performance: As part of PySpark’s distributed computing model, instr operates efficiently on big data.
2. Flexibility: Works seamlessly with other PySpark SQL functions, allowing for more complex string manipulations.
3. Accuracy: Provides an exact position of a substring, ensuring pinpoint data extraction or modification.
4. Simplification: Reduces the need for complex regex patterns in many substring search scenarios.

Spark important urls to refer

1. Spark Examples
2. PySpark Blogs
3. Bigdata Blogs
4. Spark Interview Questions
5. Official Page